Publications of Josiane Zerubia

Result of the query in the list of publications :

• 59 Articles
• 173 Conference articles
• 64 Technical and Research Reports
• 7 Collection articles or Books chapters
• 3 Books

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173 Conference articles

2 - Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In EURASIP, Bucarest, Romania, August 2012. @INPROCEEDINGS{EURASIP12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Classification of multi-sensor remote sensing images using an adaptive hierarchical Markovian model}, year = {2012}, month = {August}, booktitle = {EURASIP}, address = {Bucarest, Romania}, url = {http://hal.inria.fr/hal-00723286}, keyword = {} }

3 - A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction. K. Kayabol and J. Zerubia. In IEEE International Geoscience and Remote Sensing Symposium, pages 4054-4057, Munich, Germany, July 2012. @INPROCEEDINGS{IGARSS12, author = {Kayabol, K. and Zerubia, J.}, title = {A Comparison of Texture and Amplitude based Unsupervised SAR Image Classifications for Urban Area Extraction}, year = {2012}, month = {July}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, pages = {4054-4057}, address = {Munich, Germany}, url = {http://hal.inria.fr/hal-00697172}, keyword = {} }

4 - An hierarchical approach for model-based classification of SAR images. K. Kayabol and J. Zerubia. In 20th Signal Processing and Communications Applications Conference, Mugla, Turkey, April 2012. @INPROCEEDINGS{Kayabol12, author = {Kayabol, K. and Zerubia, J.}, title = {An hierarchical approach for model-based classification of SAR images}, year = {2012}, month = {April}, booktitle = {20th Signal Processing and Communications Applications Conference}, address = {Mugla, Turkey}, url = {http://hal.inria.fr/hal-00686658}, keyword = {} }

5 - Multichannel hierarchical image classification using multivariate copulas. A. Voisin and V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In IS&T/SPIE Electronic Imaging – Computational Imaging X, San Francisco, United States, January 2012. @INPROCEEDINGS{SPIE12, author = {Voisin, A. and Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, title = {Multichannel hierarchical image classification using multivariate copulas}, year = {2012}, month = {January}, booktitle = {IS&T/SPIE Electronic Imaging – Computational Imaging X}, address = {San Francisco, United States}, url = {http://dx.doi.org/10.1117/12.917298}, keyword = {} }

6 - Synthetic Aperture Radar Image Classification via Mixture Approaches. V. Krylov and J. Zerubia. In Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, Israel, November 2011. Keywords : Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution. Copyright : IEEE @INPROCEEDINGS{krylovCOMCAS11, author = {Krylov, V. and Zerubia, J.}, title = {Synthetic Aperture Radar Image Classification via Mixture Approaches}, year = {2011}, month = {November}, booktitle = {Proc. IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS)}, address = {Tel Aviv, Israel}, url = {http://www.ortra.biz/comcas/}, pdf = {http://hal.archives-ouvertes.fr/inria-00625551/en/}, keyword = {Synthetic Aperture Radar (SAR), remote sensing, high resolution, Classification, finite mixture models, generalized gamma distribution} } Abstract : In this paper we focus on the fundamental synthetic aperture radars (SAR) image processing problem of supervised classification. To address it we consider a statistical finite mixture approach to probability density function estimation. We develop a generalized approach to address the problem of mixture estimation and consider the use of several different classes of distributions as the base for mixture approaches. This allows performing the maximum likelihood classification which is then refined by Markov random field approach, and optimized by graph cuts. The developed method is experimentally validated on high resolution SAR imagery acquired by Cosmo-SkyMed and TerraSAR-X satellite sensors.

7 - Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil. J. Zhou and C. Proisy and X. Descombes and J. Zerubia and G. Le Maire and Y. Nouvellon and P. Couteron. In Asian Conference on Pattern Recognition (ACPR), Beijing, China, November 2011. Keywords : tree detection, Eucalyptus plantation, Marked point process, multi-date detection. @INPROCEEDINGS{Zhou11, author = {Zhou, J. and Proisy, C. and Descombes, X. and Zerubia, J. and Le Maire, G. and Nouvellon, Y. and Couteron, P.}, title = {Tree crown detection in high resolution optical images during the early growth stages of eucalyptus plantations in Brazil}, year = {2011}, month = {November}, booktitle = {Asian Conference on Pattern Recognition (ACPR)}, address = {Beijing, China}, url = {http://hal.inria.fr/hal-00740973}, keyword = {tree detection, Eucalyptus plantation, Marked point process, multi-date detection} } Abstract : Individual tree detection methods are more and more present, and improve, in forestry and silviculture domains with the increasing availability of satellite metric imagery. Automatic detection on these very high spatial resolution images aims to determine the tree positions and crown sizes. In this paper, we used a mathematical model based on marked point processes, which showed advantages w.r.t. several individual tree detection algorithms for plantations, to analyze the eucalyptus plantations in Brazil, with 2 optical images acquired by the WorldView-2 satellite. A tentative detection simultaneously with 2 images of different dates (multi-date) was tested for the first time, which estimates individual tree crown variation during these dates. The relevance of detection was discussed considering the detection performance in tree localizations and crown sizes. Then, tree crown growth was deduced from detection results and compared with the expected dynamics of corresponding populations.

8 - Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images. S. Prigent and D. Zugaj and X. Descombes and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. @INPROCEEDINGS{prigent11a, author = {Prigent, S. and Zugaj, D. and Descombes, X. and Martel, P. and Zerubia, J.}, title = {Estimation of an optimal spectral band combination to evaluate skin disease treatment efficacy using multi-spectral images}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, pdf = {http://hal.inria.fr/docs/00/59/06/94/PDF/icip_final.pdf}, keyword = {} } Abstract : Clinical evaluation of skin treatments consists of two steps. First, the degree of the disease is measured clinically on a group of patients by dermatologists. Then, a statistical test is used on obtained set of measures to determine the treatment efficacy. In this paper, a method is proposed to automatically measure the severity of skin hyperpigmentation. After a classification step, an objective function is designed in order to obtain an optimal linear combination of bands defining the severity criterion. Then a hypothesis test is deployed on this combination to quantify treatment efficacy.

9 - A fast multiple birth and cut algorithm using belief propagation. A. Gamal Eldin and X. Descombes and Charpiat G. and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation. @INPROCEEDINGS{MBC_ICIP11, author = {Gamal Eldin, A. and Descombes, X. and G., Charpiat and Zerubia, J.}, title = {A fast multiple birth and cut algorithm using belief propagation}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592446/fr/}, keyword = {Multiple Birth and Cut, multiple object extraction, Graph Cut, Belief Propagation} } Abstract : In this paper, we present a faster version of the newly proposed Multiple Birth and Cut (MBC) algorithm. MBC is an optimization method applied to the energy minimization of an object based model, defined by a marked point process. We show that, by proposing good candidates in the birth step of this algorithm, the speed of convergence is increased. The algorithm starts by generating a dense configuration in a special organization, the best candidates are selected using the belief propagation algorithm. Next, this candidate configuration is combined with the current configuration using binary graph cuts as presented in the original version of the MBC algorithm. We tested the performance of our algorithm on the particular problem of counting flamingos in a colony, and show that it is much faster with the modified birth step.

10 - SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities. K. Kayabol and A. Voisin and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), pages 173-176, Brussels, Belgium, September 2011. Keywords : High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm. @INPROCEEDINGS{inria-00592252, author = {Kayabol, K. and Voisin, A. and Zerubia, J.}, title = {SAR image classification with non- stationary multinomial logistic mixture of amplitude and texture densities}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, pages = {173-176}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00592252/en/}, keyword = {High resolution SAR images, Classification, Texture, Multinomial logistic, Classification EM algorithm} } Abstract : We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nakagami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

11 - Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution. A. Voisin and V. Krylov and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Bordeaux, September 2011. Keywords : SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models. @INPROCEEDINGS{VoisinGretsi2011, author = {Voisin, A. and Krylov, V. and Zerubia, J.}, title = {Classification bayésienne supervisée d’images RSO de zones urbaines à très haute résolution}, year = {2011}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Bordeaux}, url = {http://hal.inria.fr/inria-00623003/fr/}, keyword = {SAR Images, Classification, Urban areas, Markov Fields, Hierarchical models} } Résumé : Ce papier présente un modèle de classification bayésienne supervisée d’images acquises par Radar à Synthèse d’Ouverture (RSO) très haute résolution en polarisation simple contenant des zones urbaines, particulièrement affectées par le bruit de chatoiement. Ce modèle prend en compte à la fois une représentation statistique des images RSO par modèle de mélanges finis et de copules, et une modélisation contextuelle à partir de champs de Markov hiérarchiques. Abstract : This paper deals with the Bayesian classification of single-polarized very high resolution synthetic aperture radar (SAR) images that depict urban areas. The difficulty of such a classification relies in the significant effects of speckle noise. The model considered here takes into account both statistical modeling of images via finite mixture models and copulas, and contextual modeling thanks to hierarchical Markov random fields

12 - Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar. Y. Verdié and F. Lafarge and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011. Keywords : 3D-Modeling, shape analysis, Mesh processing. @INPROCEEDINGS{VerdieICIP11, author = {Verdié, Y. and Lafarge, F. and Zerubia, J.}, title = {Generating compact meshes under planar constraints: an automatic approach for modeling buildings lidar}, year = {2011}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Brussels, Belgium}, url = {http://hal.inria.fr/inria-00605623/fr/}, keyword = {3D-Modeling, shape analysis, Mesh processing} } Abstract : We present an automatic approach for modeling buildings from aerial LiDAR data. The method produces accurate, watertight and compact meshes under planar constraints which are especially designed for urban scenes. The LiDAR point cloud is classified through a non-convex energy minimization problem in order to separate the points labeled as building. Roof structures are then extracted from this point subset, and used to control the meshing procedure. Experiments highlight the potential of our method in term of minimal rendering, accuracy and compactness

13 - Morphological road segmentation in urban areas from high resolution satellite images. R. Gaetano and J. Zerubia and G. Scarpa and G. Poggi. In International Conference on Digital Signal Processing, Corfu, Greece, July 2011. Keywords : Segmentation, Classification, skeletonization , pattern recognition, shape analysis. @INPROCEEDINGS{GaetanoDSP, author = {Gaetano, R. and Zerubia, J. and Scarpa, G. and Poggi, G.}, title = {Morphological road segmentation in urban areas from high resolution satellite images}, year = {2011}, month = {July}, booktitle = {International Conference on Digital Signal Processing}, address = {Corfu, Greece}, url = {http://hal.inria.fr/inria-00618222/fr/}, keyword = {Segmentation, Classification, skeletonization , pattern recognition, shape analysis} } Abstract : High resolution satellite images provided by the last generation sensors significantly increased the potential of almost all the image information mining (IIM) applications related to earth observation. This is especially true for the extraction of road information, task of primary interest for many remote sensing applications, which scope is more and more extended to complex urban scenarios thanks to the availability of highly detailed images. This context is particularly challenging due to such factors as the variability of road visual appearence and the occlusions from entities like trees, cars and shadows. On the other hand, the peculiar geometry and morphology of man-made structures, particularly relevant in urban areas, is enhanced in high resolution images, making this kind of information especially useful for road detection. In this work, we provide a new insight on the use of morphological image analysis for road extraction in complex urban scenarios, and propose a technique for road segmentation that only relies on this domain. The keypoint of the technique is the use of skeletons as powerful descriptors for road objects: the proposed method is based on an ad-hoc skeletonization procedure that enhances the linear structure of road segments, and extracts road objects by first detecting their skeletons and then associating each of them with a region of the image. Experimental results are presented on two different high resolution satellite images of urban areas.

14 - A novel algorithm for occlusions and perspective effects using a 3d object process. A. Gamal Eldin and X. Descombes and J. Zerubia. In ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing), Prague, Czech Republic, May 2011. Keywords : Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting. @INPROCEEDINGS{ICASSP_2011, author = {Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, title = {A novel algorithm for occlusions and perspective effects using a 3d object process}, year = {2011}, month = {May}, booktitle = {ICASSP 2011 (International Conference on Acoustics, Speech and Signal Processing)}, address = {Prague, Czech Republic}, url = {http://hal.inria.fr/inria-00592449/fr/}, keyword = {Occlusions, 3D object process, multiple object extraction, Multiple Birth and Death, Penguins Counting} } Abstract : In this paper, we introduce a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying in the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. These configurations are projected onto the image plane. The configurations are modified until convergence using the multiple birth and death optimization algorithm and by measuring the similarity between the projected image of the configuration and the real image. During optimization, the proposed configuration is modeled by a mixed graph which represents all dependencies between the objects, including interaction between neighbor objects and parent-child dependency for occluded objects. We tested our model on synthetic image, and real images.

15 - Wavefront sensing for aberration modeling in fluorescence MACROscopy. P. Pankajakshan and A. Dieterlen and G. Engler and Z. Kam and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Chicago, USA, April 2011. Keywords : fluorescence MACROscopy , phase retrieval, field aberration. @INPROCEEDINGS{PanjakshanISBI2011, author = {Pankajakshan, P. and Dieterlen, A. and Engler, G. and Kam, Z. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Wavefront sensing for aberration modeling in fluorescence MACROscopy}, year = {2011}, month = {April}, booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, address = {Chicago, USA}, url = {http://hal.inria.fr/inria-00563988/en/}, keyword = {fluorescence MACROscopy , phase retrieval, field aberration} } Abstract : In this paper, we present an approach to calculate the wavefront in the back pupil plane of an objective in a fluorescent MACROscope. We use the three-dimensional image of a fluorescent bead because it contains potential pupil information in the ‘far’ out-of-focus planes for sensing the wavefront at the back focal plane of the objective. Wavefront sensing by phase retrieval technique is needed for several reasons. Firstly, the point-spread function of the imaging system can be calculated from the estimated pupil phase and used for image restoration. Secondly, the aberrations in the optics of the objective can be determined by studying this phase. Finally, the estimated wavefront can be used to correct the aberrated optical path with- out a wavefront sensor. In this paper, we estimate the wavefront of a MACROscope optical system by using Bayesian inferencing and derive the Gerchberg-Saxton algorithm as a special case.

16 - Multiple Birth and Cut Algorithm for Point Process Optimization. A. Gamal Eldin and X. Descombes and J. Zerubia. In Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS), Kuala Lumpur, Malaysia, December 2010. Keywords : Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process. @INPROCEEDINGS{MBC_MPP_SITIS10, author = {Gamal Eldin, A. and Descombes, X. and Zerubia, J.}, title = {Multiple Birth and Cut Algorithm for Point Process Optimization}, year = {2010}, month = {December}, booktitle = {Proc. IEEE International Conference on Signal-Image Technology and Internet-based Systems (SITIS)}, address = {Kuala Lumpur, Malaysia}, url = {http://hal.archives-ouvertes.fr/inria-00516305/fr/}, keyword = {Multiple Birth and Cut, Graph Cut, Multiple Birth and Death, Marked point process} } Abstract : In this paper, we describe a new optimization method which we call Multiple Birth and Cut (MBC). It combines the recently developed Multiple Birth and Death (MBD) algorithm and the Graph-Cut algorithm. MBD and MBC optimization methods are applied to the energy minimization of an object based model, the marked point process. We compare the MBC to the MBD showing the advantages and disadvantages, where the most important advantage is the reduction of the number of parameters. We validated our algorithm on the counting problem of flamingos in colony, where our algorithm outperforms the performance of the MBD algorithm.

17 - A theoretical and numerical study of a phase field higher-order active contour model of directed networks. A. El Ghoul and I. H. Jermyn and J. Zerubia. In The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, November 2010. Keywords : Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing. Copyright : Springer-Verlag GmbH Berlin Heidelberg @INPROCEEDINGS{Elghoul10b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A theoretical and numerical study of a phase field higher-order active contour model of directed networks}, year = {2010}, month = {November}, booktitle = {The Tenth Asian Conference on Computer Vision (ACCV)}, address = {Queenstown, New Zealand}, pdf = {http://hal.archives-ouvertes.fr/inria-00522443/fr/}, keyword = {Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing} } Abstract : We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest.We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images.

18 - Point-spread function model for fluorescence MACROscopy imaging. P. Pankajakshan and Z. Kam and A. Dieterlen and G. Engler and L. Blanc-Féraud and J. Zerubia and J.C. Olivo-Marin. In Asilomar Conference on Signals, Systems and Computers, pages 1364-136, Pacific Grove, CA, USA , November 2010. Keywords : fluorescence MACROscopy , point-spread function, pupil function, vignetting . @INPROCEEDINGS{PanjakshanASILOMAR2010, author = {Pankajakshan, P. and Kam, Z. and Dieterlen, A. and Engler, G. and Blanc-Féraud, L. and Zerubia, J. and Olivo-Marin, J.C.}, title = {Point-spread function model for fluorescence MACROscopy imaging}, year = {2010}, month = {November}, booktitle = {Asilomar Conference on Signals, Systems and Computers}, pages = {1364-136}, address = {Pacific Grove, CA, USA }, url = {http://hal.inria.fr/inria-00555940/}, keyword = {fluorescence MACROscopy , point-spread function, pupil function, vignetting } } Abstract : In this paper, we model the point-spread function (PSF) of a fluorescence MACROscope with a field aberration. The MACROscope is an imaging arrangement that is designed to directly study small and large specimen preparations without physically sectioning them. However, due to the different optical components of the MACROscope, it cannot achieve the condition of lateral spatial invariance for all magnifications. For example, under low zoom settings, this field aberration becomes prominent, the PSF varies in the lateral field, and is proportional to the distance from the center of the field. On the other hand, for larger zooms, these aberrations become gradually absent. A computational approach to correct this aberration often relies on an accurate knowledge of the PSF. The PSF can be defined either theoretically using a scalar diffraction model or empirically by acquiring a three-dimensional image of a fluorescent bead that approximates a point source. The experimental PSF is difficult to obtain and can change with slight deviations from the physical conditions. In this paper, we model the PSF using the scalar diffraction approach, and the pupil function is modeled by chopping it. By comparing our modeled PSF with an experimentally obtained PSF, we validate our hypothesis that the spatial variance is caused by two limiting optical apertures brought together on different conjugate planes.

19 - Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images. S. Ben Hadj and F. Chatelain and X. Descombes and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010. Keywords : Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM). @INPROCEEDINGS{sbenhadj10a, author = {Ben Hadj, S. and Chatelain, F. and Descombes, X. and Zerubia, J.}, title = {Parameter estimation for a marked point process within a framework of multidimensional shape extraction from remote sensing images}, year = {2010}, month = {September}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, url = {http://hal.archives-ouvertes.fr/docs/00/52/63/45/PDF/ISPRS_SBH_FC_XD_JZ_Final2.pdf}, keyword = {Shape extraction, Marked point process, RJMCMC, Simulated Annealing, Stochastic EM (SEM)} }

20 - Tree crown detection in high resolution optical and LiDAR images of tropical forest. J. Zhou and C. Proisy and X. Descombes and I. Hedhli and N. Barbier and J. Zerubia and J.-P. Gastellu-Etchegorry and P. Couteron. In Proc. SPIE Symposium on Remote Sensing, Toulouse, France, September 2010. Keywords : Tropical forest, tree detection, Marked point process. @INPROCEEDINGS{Zhou10, author = {Zhou, J. and Proisy, C. and Descombes, X. and Hedhli, I. and Barbier, N. and Zerubia, J. and Gastellu-Etchegorry, J.-P. and Couteron, P.}, title = {Tree crown detection in high resolution optical and LiDAR images of tropical forest}, year = {2010}, month = {September}, booktitle = {Proc. SPIE Symposium on Remote Sensing}, address = {Toulouse, France}, url = {http://dx.doi.org/10.1117/12.865068}, keyword = {Tropical forest, tree detection, Marked point process} }

21 - Multi-spectral Image Analysis for Skin Pigmentation Classification. S. Prigent and X. Descombes and D. Zugaj and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, September 2010. Keywords : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction. @INPROCEEDINGS{sp02, author = {Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, title = {Multi-spectral Image Analysis for Skin Pigmentation Classification}, year = {2010}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {Hong-Kong, China}, pdf = {http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, keyword = {skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} } Abstract : In this paper, we compare two different approaches for semi-automatic detection of skin hyper-pigmentation on multi-spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.

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